Reviewer Guide

Based on the ICLR 2025 Reviewer Guide

Thank you for agreeing to serve as an MLMP ICLR 2025 reviewer! Your contribution as a reviewer is paramount to creating an exciting and high-quality program. Reviews are also very valuable for helping authors improve their work, sometimes even more valuable than the feedback they receive at the event itself. This is especially important for the workshop – a venue explicitly designated for supporting work in progress rather than selectivity.

Reviewing a submission: step-by-step

• Read the paper: It’s important to carefully read through the entire paper, and to look up any related work and citations that will help you comprehensively evaluate it. Be sure to give yourself sufficient time for this step.
• While reading, consider the following:
  1. Objective of the work: What is the goal of the paper? Is it to better address a known application or problem, draw attention to a new application or problem, or to introduce and/or explain a new theoretical finding? A combination of these? Different objectives will require different considerations as to potential value and impact.
  2. Strong points: is the submission relevant, clear, technically correct, experimentally rigorous, reproducible, does it present novel findings (e.g. theoretically, algorithmically, etc.)?
  3. Weak points: is it weak in any of the aspects listed in the previous bullet?
  4. Be mindful of potential biases and try to be open-minded about the value and interest a paper can hold for the entire multiscale ML community, even if it may not be very interesting for you.
• Grade
  1. Read the description of the track to which the paper was submitted
  2. Grade according to four criteria:
     1. Relevance. Does the submission contribute to building an AI that can advance from low–level theory and computationally–expensive simulation code to modeling complex systems on a useful time scale?
     2. Technical quality. Does the paper support the claims? This includes determining if results, whether theoretical or empirical, are correct and if they are scientifically rigorous. Is the approach well motivated, including being well-placed in the literature? Providing the source code for review is a plus.
     3. Novelty. Does the approach differ from the prior work?
     4. Significance. Does it contribute new knowledge and sufficient value to the community? Note, this does not necessarily require state-of-the-art results. Submissions bring value to the multiscale ML community when they convincingly demonstrate new, relevant, impactful knowledge (incl., empirical, theoretical, for practitioners, etc). Publishing source code and data is a plus.
• Write and submit your review, organizing it as follows:
  1. Summarize what the paper claims to contribute. Be positive and constructive.
  2. List strong and weak points of the paper. Be as comprehensive as possible.
  3. Give a numeric score
  4. Provide feedback with the aim to improve the paper. Make it clear that these points are here to help, and not necessarily part of your assessment.